SCI Women’s Cancer Center Innovation Awardee

October 2023

Lei Xing, PhD, Jacob Haimson and Sarah S. Donaldson Professor, James Zou, PhD, assistant professor of biomedical data science, and Max Diehn, MD, PhD, professor of radiation oncology, were awarded a $50,000 SCI Women's Cancer Center Innovation Award for their proposal “Biology-aware deep learning for prediction of outcome of cancer immunotherapy with immune checkpoint inhibitors.” Xing focuses on artificial intelligence in medicine, medical imaging, treatment planning, image-guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology.  Zou’s work aims to make machine learning more reliable, human-compatible, and statistically rigorous and he is particularly interested in applications of machine learning to human disease and health. Diehn’s research centers on cancer stem cell biology and its implications for therapy, as well as the development of biomarkers for detecting and analyzing cancer cells. As a clinician, he treats lung cancer and is involved in various clinical studies.

Leveraging the immune system to fight cancer is highly effective for some cancer patients but is completely ineffective for others. Currently, there is no reliable way to predict whether a patient will respond.. Xing, Zou, and Diehn propose to develop a deep-learning tool to predict the response of cancer patients to immunotherapy. The tool will use patient mutation data to model how genetic pathways and protein function are altered in a patient’s tumor cells, enabling a more complex prediction mechanism than what is currently available. In pilot studies, the investigators have already shown that this approach achieves accurate predictions across nine types of cancer and outperforms state-of-the art techniques. Through the support of the SCI Women's Cancer Center Innovation Award,. Xing, Zou, and Diehn will improve and validate this tool across additional cancer types. Their hope is that this tool will help personalize treatment and significantly improve patient care.